4. Embracing Deep Learning by Andrew W. trask posted by endowment posts
if you want to take my word for it and ask me “gargeya, where do you think I should start?” she would close her eyes and point you to this book. a fantastic book on deep learning that even makes me feel why I didn’t start with this.
This book covers most of the topics you would need to get your hands dirty with deep learning and go down a positive exponential slope toward insight and insight.
You are reading: Best deep learning books
This is a summary of what you will learn: {Introduction to deep learning and why you should do it, fundamental concepts, introduction to the neural network, gradient descent in detail, visualization of neural networks, backpropagation and the concept of learning, batch processing and regularization, recurrent networks for text data, lstms, and finally federated learning}. trust me if this isn’t enough to get you started, then most things aren’t.
5. practical machine learning with scikit-learn keras and tensorflow from aurelion geron published by o` reilley
once you’re done with basic statistics, machine learning, and deep learning. now you want to up your game with practical implementations and build a complete deep learning model in tensorflow. this is the book that not only me but tensorflow itself suggests.
I personally follow this book constantly and in terms of deep learning with tensorflow this is my best book. the way the author has explained the concepts is exceptionally easy and intuitive. he made me feel more powerful every time I was done with a certain section.
the book is a treasure of knowledge with more than 800 pages on topics: {fundamentals, end-to-end ml project, detailed most common machine learning algorithms and techniques, neural network with keras, custom models and training with tensorflow, deep machine vision with convolutions, sequence models with rnns and lstms, attention models, generative learning like autoencoders and gans, reinforcement learning}.
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Learning and working in parallel with this book will completely change your skill level in machine learning and deep learning in practice. you should definitely try it.
6. deep learning by ian goodfellow, yoshua bengio and aaron courville published by mit press
let me get some facts straight, the authors of this book include the pioneers of deep learning, yoshua bengio one of the three godfathers of deep learning, ian goodfellow popular of this generative adversarial networking (gans).
This book is a legend among all books on deep learning. the book not only talks about deep learning concepts, but first reviews your knowledge and concepts of applied mathematics (linear algebra, probability and information theory, numerical computations) and basic machine learning concepts in terms of mathematics (the components lower basics of a.i.).
once you have gone through part 1, then comes part 2: a detailed study on deep learning: modern practices (deep forward network, regularization, optimization, convolutional networks, sequence modeling , applications).
After delving into all of these concepts and building deep logic and intuition about deep learning, comes Part 3 (Deep Learning Research) which includes some of the most popular research topics in deep learning. deep learning such as (probabilistic pca and factor analysis, machine encoders, structured probabilistic models for deep learning, monte carlo methods, deep generative models and so on).
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I wouldn’t recommend this book to everyone, but to those who have a special focus on deep learning and are willing to work very hard on all math and stick to deep learning.
7. deep learning for coders with fastai and pytorch by jeremy howard & sylvain gugger posted by o`reilley
saving the best complete package resource for last. this book is definitely in my top 3 favorite books, absolutely beautiful not only in terms of deep learning, but also in all other very important factors that are related to deep learning in practice, such as model for production, ethics of data and your deep learning journey. (a map to follow). These three things are really very important if you hope to become a deep learning engineer or anything remotely similar in practice.
supported by the entire fast.ai website to teach people deep learning from scratch for free with comprehensive video tutorials, labs on the paperspace deep learning environment, introduction to a very powerful library for learning deep into pytorch, i.e. fastai. the book is detailed enough with so much practical content that you will definitely learn something new after each reading.
let me give you a glimpse of what you will see inside this book {fastai applications including image classification, next generation model training, collaborative filtering, deep analysis, tabular modeling, pnl deep analysis, then comes the language model from scratch, cnns architecture like resnets and all other very essential deep learning architectures from scratch} I highly recommend it to people interested in deep learning.
That’s all for this article, I hope you now waste less time wandering and getting confused and start with whichever of the books you loved the most. keep growing my partner a.i. community members.
gargeya sharma
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